A project by the Data Science Working Group @ Code for San Francisco Project Lead: Jude Calvillo
The aim of this project is to develop a highly versatile temporal-spatial anomaly detection app for all levels and agencies of government (and NGOs). Such an anomaly detector would not just reveal truly significant events (or summary events), given such events' underlying seasonality and trend, but it would also provide time-series and local news context. Thus, it would point civic authorities to events that actually warrant a tactical response, and it would better prepare them for that response, all of which would improve efficiency (saving time, funds, etc).
Abstraction and Parameterization of Existing App
This app will be an abstraction of the DSWG's Dept. of Transportation Hazmat Incident Anomaly Detector, where its interface and purported relevance will be generalized, while its inputs and parameters will be as follows:
Responsible DSWG Teammates
Status, as of Oct. 25, 2016
Shiny, Shiny app, anomaly detection, Leaflet, data science working group, code for san francisco, R, r programming, jude calvillo, inferential statistics, code for america, brigade